Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm.
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| Title: | Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm. |
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| Authors: | Shabri, Ani1 ani@utm.my, Samsudin, Ruhaidah2 ruhaidah@utm.my |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Dec2025, Vol. 15 Issue 6, p5854-5862. 9p. |
| Subjects: | Load forecasting (Electric power systems), Bees algorithm, Data analysis, Seasonal temperature variations, Forecasting, Fluctuations (Physics) |
| Geographic Terms: | Malaysia |
| Abstract: | Nonlinear and seasonal fluctuations present significant challenges in predicting electricity load. To address this, a combination weighted forecast model (CWFM) based on individual prediction models is proposed. The artificial bee colony (ABC) algorithm is used to optimize the weighted coefficients. To evaluate the model's performance, the novel CWFM and three benchmark models are applied to forecast electricity load in Malaysia and Thailand. Performance is assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results indicate that the proposed combined model outperforms the single models, demonstrating improved accuracy and better capturing seasonal variations in electricity load. The ABC algorithm helps in finding the optimal combination of weights, ensuring that the model adapts effectively to different forecasting scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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